The present invention relates to medical imaging of the heart, and more particularly, to automatic detection of the left ventricle in 2D magnetic resonance images.
Cardiovascular disease is the leading cause of death in developed countries. Early diagnosis can be effective in reducing the mortality of cardiovascular disease. Magnetic resonance imaging (MRI) can accurately depict cardiac structure, function, perfusion, and myocardial viability with a capacity unmatched by any other imaging modality. Accordingly, MRI is widely accepted as the gold standard for heart chamber quantification, which means that measurements extracted using other imaging modalities, such as echocardiography and computed tomography (CT), typically must be verified using MRI. Quantification of the left ventricle (LV) is of particular interest among the four heart chambers because it pumps oxygenated blood from the heart to the rest of the body. In order to quantify functional measurements of the LV, it is necessary to detect or segment the LV in an MRI image.
Automatic LV detection in MRI images is a challenging problem due to large variations in orientation, size, shape, and image intensity of the LV. First, unlike CT, MRI is flexible in selecting the orientation of the imaging plane, and this helps cardiologists to capture the best view for diagnosis. However, this flexibility presents a large challenge for automatic LV detection because both the position and orientation of the LV are unconstrained in an image. The LV is a roughly rotation symmetric object around its long axis, which is generally defined as the axis connecting the LV apex to the center of the mitral valve. Long-axis views (where the imaging plane passes through the LV long axis) are often captured to perform LV measurement. However, the orientation of the LV long axis in the image is unconstrained. Second, an MRI image only captures a 2D intersection of a 3D object, therefore information is lost compared to a 3D volume. The image plane can be rotated to get several standard cardiac views, such as the apical-two-chamber (A2C) view, the apical-three-chamber (A3C), the apical-four-chamber (A4C), and the apical-five-chamber (A5C) view. However, this view information is not available to help automatic LV detection. Although the LV and right ventricle (RV) have quite different 3D shapes, in the 2D A4C view, the LV is likely to be confused with the RV. Third, the LV shape changes significantly in a cardiac cycle. The heart is a non-rigid shape, which changes shape as it beats to pump blood to the body. In order to study the dynamics of the heart, a cardiologist needs to capture images from different cardiac phases. The LV shape changes significantly from the end-diastolic (ED) phase (when the LV is the largest) to the end-systolic (ES) phase (when the LV is the smallest). Finally, MRI images captured with different scanners or different imaging protocols have large variations in intensity. Accordingly, an automatic LV detection method which overcomes the above challenges is desirable.